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Estimating the Region of Attraction Using Polynomial Optimization: A Converse Lyapunov Result

机译:使用多项式优化估算吸引力区域:逆转Lyapunov结果

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In this paper, we propose an iterative method for using SOS programming to estimate the region of attraction of a polynomial vector field, the conjectured convergence of which necessitates the existence of polynomial Lyapunov functions whose sublevel sets approximate the true region of attraction arbitrarily well. The main technical result of the paper is the proof of existence of such a Lyapunov function. Specifically, we use the Hausdorff distance metric to analyze convergence and in the main theorem demonstrate that the existence of an n-times continuously differentiable maximal Lyapunov function implies that for any ε > 0, there exists a polynomial Lyapunov function and associated sub-level set which together prove stability of a set which is within ε Hausdorff distance of the true region of attraction. The proposed iterative method and probably convergence is illustrated with a numerical example.
机译:在本文中,我们提出了一种迭代方法,用于使用SOS编程来估计多项式矢量场的吸引区域,其猜测会聚需要存在的多项式Lyapunov函数,其上尺度良好地近似于真正的吸引区域。本文的主要技术结果是存在这种Lyapunov功能的证明。具体而言,我们使用Hausdorff距离度量来分析收敛,并且在主定理中表明,存在n次连续微分的最大Lyapunov函数的存在意味着对于任何ε> 0,存在多项式Lyapunov函数和相关子级集合这在一起证明了一套的稳定性,这在真正的吸引区域的εausdorff距离内。提出的迭代方法和可能会聚是用数值示例说明的。

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